Triple
T20280054
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Farewell to the Master |
E503114
|
entity |
| Predicate | hasWordCountRange |
P7605
|
FINISHED |
| Object | novelette-length |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: novelette-length | Statement: [Farewell to the Master, hasWordCountRange, novelette-length]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWordCountRange Context triple: [Farewell to the Master, hasWordCountRange, novelette-length]
-
A.
wordCount
chosen
Indicates the total number of words contained in a given text or linguistic unit.
-
B.
hasLengthRange
Indicates that an entity’s length falls within a specified minimum-to-maximum range.
-
C.
hasApproximateNumberOfAttestedWords
Indicates that an entity is associated with an estimated or approximate count of words that are documented or attested for it.
-
D.
hasApproximateNumberOfLetters
Indicates that an entity is associated with a number that roughly, but not exactly, corresponds to the count of letters it contains.
-
E.
hasLetterCount
Indicates that an entity is associated with a specific number representing how many letters it contains.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69e0b4b0e79c8190bd61f22ef1329fa8 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6768ddfd0819098b2cc7fed0f4fe2 |
completed | April 20, 2026, 6:55 p.m. |
| PD | Predicate disambiguation | batch_69e55b1e5e1c8190ba8a5544b1db9e1d |
completed | April 19, 2026, 10:45 p.m. |
Created at: April 16, 2026, 10:36 a.m.